Performance Parameters Under Different Loads for Hybrid-Cloud Scheduling

Zhigang Zhang published a dataset on figshare in 2026 containing performance parameters for a hybrid-cloud resource scheduling algorithm. The data, stored in an XLS file, is 5.5 KB in size and compares the proposed EMPA-ASA algorithm against baselines like GA and PSO. Results show performance across QoS metrics including end-to-end delay, response time, throughput, and packet-loss rate.

Use Cases

• Benchmarking hybrid-cloud scheduling algorithms based on reported QoS metrics like end-to-end delay and response time.
• Analyzing cost-performance trade-offs in cloud systems based on the reported 48% and 70% cost reductions.
• Modeling system performance under high-load scenarios based on the described load adaptivity.
• Training or validating queueing models for cloud resource management based on the M/M/c model mentioned.

Strengths

• Dataset is small (5.5 KB), facilitating quick download and inspection.
• Results include specific quantitative comparisons, such as 48% and 70% cost reductions relative to GA and PSO.
• Performance is evaluated across multiple concrete QoS metrics: end-to-end delay, response time, throughput, and packet-loss rate.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset appears to be a specific performance evaluation output, limiting its generalizability.

Provenance

Source

Zhigang Zhang via figshare.

Collection Method

Likely generated from performance evaluations of a prototype hybrid-cloud scheduling algorithm.

Time Range

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Freshness

Last updated 2026-04-10 17:47:01; freshness should be verified.

Geography

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